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Translating embeddings for modeling multi-relational data
| Venue: | In Advances in Neural Information Processing Systems 26. Curran Associates, Inc |
| Citations: | 24 - 0 self |
BibTeX
@INPROCEEDINGS{Bordes_translatingembeddings,
author = {Antoine Bordes and Nicolas Usunier and Jason Weston and Oksana Yakhnenko},
title = {Translating embeddings for modeling multi-relational data},
booktitle = {In Advances in Neural Information Processing Systems 26. Curran Associates, Inc},
year = {},
pages = {2787--2795}
}
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Abstract
We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces. Our objective is to propose a canonical model which is easy to train, contains a reduced number of parameters and can scale up to very large databases. Hence, we propose TransE, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities. Despite its simplicity, this assump-tion proves to be powerful since extensive experiments show that TransE signif-icantly outperforms state-of-the-art methods in link prediction on two knowledge bases. Besides, it can be successfully trained on a large scale data set with 1M entities, 25k relationships and more than 17M training samples. 1
